Revisiting Derivative Based Methods on QRS Detections from an ECG Signal

The computation of heartbeat counts is a primary condition for different ECG signals. Regardless of power supply noise, body movements, baseline drift, or high impedance, the algorithm can reliably detect a peak for the measurement of heartbeat count. Initially, a few pre-processing measures are needed, such as removing hum noise from the data acquisition system and instrumentation due to improper grounding. Every heartbeat is made up of P, QRS complex, and T waves, each with its amplitude and duration. The first and second derivative-based methods for detecting QRS peaks from an ECG signal are revisited in this paper. QRS peak detection algorithms diagnose heart diseases like heart rate variation, tachycardia, and bradycardia. The patients’ ECG records were obtained from the Apnea-ECG database. The accuracy of derivative-based approaches in detecting QRS peaks is demonstrated by simulation performance. For experimental simulations with an ECG signal, the Matlab 9.4 tool is used.